Stankovic, L. Chenyang, T. Abdelzaher, A spatiotemporal protocol for wireless sensor network. Parallel Distrib. Stankovic, T. Abdelzaher, C. Lu, L. Sha, J. Hou, Real-time communication and coordination in embedded sensor networks. IEEE 91, — 4. Sohrabi, J. Pottie, Protocols for self-organization of wireless sensor network. Karp, H. Bose, P.
Morin, I. Stojmenovi, J. Lu, B. Blum, T. Abdelzaher, J. Felemban, C. Lee, E. Chipara, Z. He, G. Xing, Q. Chen, X. Wang, C. Lu, J. Mahapatra, K. Anand, D. Agrawal, QoS and energy aware routing for real-time traffic in wireless sensor networks. Tran, H. Sankarasubramaniam, B. Akan, and I. Wu, B. Cho, S. Lim, M. Wu, K. Li, W. Hsu, B. Krishnamachari, A. Seada, M. Zuniga, A. Helmy, B. Krishnamachari, Energy efficient forwarding strategies for geographic routing in lossy wireless sensor networks, in Proceedings of the ACM Sensor Systems , pp.
Razzaque, M. Alam, C. Hong, Multi-constrained QoS geographic routing for heterogeneous traffic in sensor networks. Zeng, K. Ren, W. Lou, P. Moran, Energy aware efficient geographic routing in lossy wireless sensor networks with environmental energy supply. Chen, V. Leung, S. Mao, Y. Xiao, I. Chlamtac, Hybrid geographical routing for flexible energy-delay trade-offs. Sharif, V. Potdar, A. E Rusli, R.
Harris, A. Koulali, A. Kobbane, M. El Koutbi, M. Wang, M. C Vuran, S. Goddard: Cross-layer analysis of the end-to-end delay distribution in wireless sensor networks. Ehsan, B. Hamdaoui, M. Guizani, Radio and medium access contention aware routing for lifetime maximization in multichannel sensor networks. Park, Z. Mir, N. Kim, C. Aissani, A. Mellouk, N. Badache, B. Park, Y. Ham, S. Park, J. Woo, J. Oh, Y. Yim, J. Lee, H. Park, S. Kim, A reliable communication strategy for real-time data dissemination in wireless sensor networks, in Proceedings of the IEEE 26th International Conference on Advanced Information Networking and Applications , pp.
Tavallaie, H. Naji, M. Sabaei, N. Yim, H. Lee, S. Oh, S. Spohn, J. Calinescu, Computing 2-hop neighborhoods in ad hoc wireless networks, in Proceedings of the Ad Hoc Now , pp. Li, Y. Quang, D. Kim, Enhancing real-time delivery of gradient routing for industrial wireless sensor networks. Onindustrial Inform. Jung, S. Park, E. Diop, C. Pham, O. Thiare, 2-hop neighborhood information for cover set selection in mission-critical surveillance with wireless image sensor networks, in Proceedings of the Wireless Days WD , pp.
Shiva, K. Raja, K. Venugopal, S. Iyengar, L. He, C. Huang, B. Blum, J. Abdelzaher, Range-free localization and its impact on large scale sensor networks. ACM Trans. Roosta, M. Menzo, S. Woo, D. Culler, Evaluation of efficient link reliability estimators for low-power wireless networks. Technical report, University of California The protocol delivers fault tolerance and energy efficiency by means of a dual cluster head scheme and guarantees the desired QoS by considering delay and bandwidth parameters in the route selection process.
In each cluster, sensor nodes are delegated different roles, such as cluster head or ordinary member node. A cluster head CH is elected in each cluster that collects sensed data from member nodes, aggregates and transmits the aggregated data to the next cluster head or to the base station BS. The role of ordinary member node is to sense data from the environment and communicate the data to the cluster head as shown in Fig.
The QBCDCP protocol [1] achieves QoS routing in Wireless Sensor Networks by using delay, along with the transmission energy, as the routing metric while ensuring that bandwidth requirements and end-to-end delay objectives of the application are met in the route selection process. The protocol achieves energy efficiency through a rotating cluster head mechanism and delegation of energy intensive tasks to a single high power Base Station.
The QBCDCP scheme shows an increase in sensing node lifetime with the number of clusters, but with a corresponding increase in end-to-end delay. The cluster based network model provides inherent optimization capabilities at cluster heads, such as data fusion and reduces communication interference by using Reprinted by permission from Springer Nature: Springer LNEE, Shiva Prakash T.
High energy nodes can be used to process and send the information while low energy nodes can be used to perform the sensing task. Overall, clustering is an excellent approach for achieving scalability, lifetime, energy efficiency, and reduce network contention.
While earlier works were primarily focused on the above mentioned aspects, more recent research has begun to consider fault tolerance, reliability, and Quality of Service and our proposed protocol is motivated by these metrics. The proposed algorithm Fault Tolerant QoS Adaptive Clustering FTQAC employs a fault tolerant dual cluster head mechanism in the cluster with respect to the working of the cluster head and guarantees the desired QoS by including delay and bandwidth parameters in the route selection process.
Furthermore, the protocol evenly distributes the energy consumption to all nodes so as to extend the sensor network lifetime. A self-organizing, adaptive clustering scheme that uses randomized rotation of cluster heads to uniformly distribute the energy load among the sensor nodes in the network is proposed in Low-Energy Adaptive Clustering Hierarchy LEACH [2]. The cluster heads have the responsibility of collecting data from their clusters and 3.
After cluster formation, cluster heads creates a transmission schedule and broadcasts it to all the nodes in their respective cluster. This schedule contains TDMA slots for each neighboring node. This scheduling scheme helps energy minimization at nodes that can power off their radio during all but their scheduled time slot.
Cluster head selection that uses probability does not naturally lead to minimum energy consumption. Cluster head route messages to the Base Station in a single hop and when the network size grows, it is possible that these cluster heads discharge faster than others and if the distance is large, the messages may not reach the Base Station.
The two protocols nominate the transmitting nodes by using threshold schemes. The deficiency of the two schemes are the overhead related to forming of clusters at multiple levels and the process of executing threshold based methods.
Instead of classifying nodes into clusters, the scheme makes a chain of sensor nodes. As per this structure, each node transmits to and receives from only the nearest nodes of its neighbors. The node carrying out data aggregation transmits the data to the node that communicates with the sink. Every round, a greedy scheme is run to designate one node in the chain to transmit with the sink. The shortcoming of the protocol is that the single leader can itself become a congestion point in the network.
Younis et al. Stable Election Protocol [8] utilize non-homogeneous sensor nodes to dispense power uniformly in WSNs. The scheme of cluster head election is based on two distinct levels of power. A node with the maximum weight as per their different power levels is elected as cluster head.
Successive cluster heads are selected using this scheme. This ensures that cluster heads are randomly elected and power consumption is evenly distributed among nodes.
The two-level scheme of TL-LEACH lowers the amount of nodes that require to transmit to the base station, efficaciously lowering the total power usage. However, there is a huge probability of rise in overhead at the time of selection of primary and secondary cluster heads which causes higher power consumption.
The scheme can strengthen the reliability and dependability of WSN by allotting evenly the communication and data fusion load amid the cluster heads. The dual cluster head model can also enhance the life of Wireless Sensor Networks. The drawbacks of the protocol are that the secondary cluster is formed only if the number of nodes in a given cluster is larger than a threshold, the protocol proposed in this chapter always creates a secondary cluster to achieve fault tolerance in WSNs.
Muruganathan et al. BCDCP relies on the base station to perform balanced cluster formation, path selection, and other energy intensive tasks. Multi-hop communication among cluster heads is employed to reach the base station, through the lowest energy path. Haiping and Ruchuan [12] propose an innovative clustered control scheme based on location data, priority of coverage, power, and multi-layered architecture. This scheme elects a cluster head as per the geographical locations and residual power at the nodes and assures greater coverage rate for the cluster head by a priority system to evade the dense and sparse distribution of cluster heads.
This scheme lowers the power cost by expanding the size of sleeping nodes amid non-media data transmission phase and including many intermediate nodes to forward data during multimedia data transmission which enhance the lifetime of the network. Ji et al. Feng et al. EkbataniFard et al. Ben-othman et al. Higher priority queues have outright special advantage over low priority queues. Aslam et al. The protocol is used to find the largest delay and backlog limits for applications with QoS needs.
Noori et al. A scheme of the packet transmission rate of the sensors is proposed making use of Voronoi tessellation. The probability of accomplishing a given life span by individual sensors is determined and is then utilized to examine the cluster life span. The study combines the result of dynamic cluster head assignment, power model, random deployment of sensors, data compression and packet generation model at the sensors.
Yao et al. Quang and Kim et al. In addition to the cluster head, some nodes can be selected as intermediate nodes, each of which manages a sub-cluster, according to their positions. Intermediate nodes aggregate data from general nodes and send them to the cluster head. The selection of intermediate nodes to optimize energy consumption is modeled as a mixed-integer linear programming having high computational complexity; consequently, the lowest energy path searching algorithm is proposed to shorten the computational time.
Fapojuwo et al. The scheme obtains power efficiency through a revolving head clustering mechanism and assignment of power-hungry tasks to a single base station, QoS support parameters like delay and bandwidth are used for the route selection process. Prakash et al. An example scenario is shown in Fig. The sensors are grouped into one-hop clusters with a specific clustering algorithm. All sensor nodes are immobile. When this energy supply is exhausted, the sensor becomes non-operational.
All nodes are supposed to be aware of their residual energy and are capable of measuring the signal strength indicator RSSI of a received message, this measurement may be used as an indication of distance from the sender. The received signal strength indicator RSSI is a measurement of the power present in a received radio signal.
Each cluster head performs activities such as scheduling of intra-cluster and inter-cluster communications, data aggregation, and data forwarding to the base station through multi-hop routing. The role of the secondary cluster head is to emulate the role of the primary cluster head in case of its failure. On the other hand, a sensing node maybe actively sensing the target area.
The base station in turn performs the key tasks of cluster formation, cluster head selection, and cluster head to cluster head QoS routing path construction. The base station has a constant power supply and thus, has no energy constraints.
Hence, it can also be used to perform functions that are energy intensive and can store past data. The base station can transmit directly to the nodes, however the nodes due to their limited power supply may not be able to communicate with the base station directly, except the nodes close to the base station.
The objectives are to 3. Reduce the average end-to-end packet delay. Minimize the packet delivery ratio PDR. The proposed protocol FTQAC incorporates QoS requirements like fault tolerance, delay, and bandwidth information during route establishment.
The energy intensive tasks are delegated to the base station to improve the lifetime of the network. The operation of the protocol is split into phases. The first stage of FTQAC consists of the cluster splitting and primary cluster selection, the second phase involves the selection of the secondary cluster head.
The last phase involves the formation of the QoS route from cluster head to the base station. TDMA Time Division Multi Access and spreading code are engaged to minimize inter-cluster interference to allow simultaneous transmissions in neighboring clusters. The control period is used for transmission and reception of control messages related to clustering and routing information, state updates data requests and acknowledgments and neighbor discovery.
To allow simultaneous transmissions in neighboring clusters and reduce inter-cluster interference, each cluster is assigned a different spreading code assumed to be orthogonal. The sensing nodes send message M2 to the primary cluster. It selects a secondary cluster from one of the nodes which have the largest RSSI of message M1 among the qualified nodes whose residual energy is more than the average residual energy of all nodes in the cluster.
The role of the secondary cluster head is to emulate the primary cluster head in case of its failure. The secondary cluster head sends an ACK back to the primary cluster head on receiving the message. This process prevents frequent re-clustering and avoids excessive depletion of the cluster heads battery; this mechanism results in better power efficiency.
Delay and bandwidth are measured at cluster head nodes. The delay associated with traversing a particular cluster head is the time duration between entering the input queue and leaving the output queue of the cluster head 3. Bandwidth is computed at each cluster head as the number of free time slots within each cluster head BWx y.
When a connection is desired, the base station sets up a QoS-based route Q S between the cluster head where the connection is initiated through other cluster heads and finally ending at the base station as shown in Fig.
The base station finds the route which minimizes the delays and power along the path, and has a minimum bandwidth greater than or equal to the requested bandwidth BWr eq as shown in Algorithm 3. The algorithm may produce more than one optimal path; the path having cluster heads with minimum required transmission energy E aSum is chosen.
After a route is chosen, the base station communicates it to the concerned cluster head nodes, which schedule the connection by specifying the required number of time slots to maintain it.
During the communication phase, when the primary cluster head is depleted of energy it transfers its role to the secondary cluster head. The primary cluster head is currently involved in the QoS path informs both the downstream cluster head, upstream cluster head, and the base station of its duty transfer and then relinquishes its role.
The traffic is redirected to the new primary cluster and the QoS level is maintained throughout the duration of the connection.
The base station is located 25 m from the sensor field. The end-to-end delay objective Dr eq is fixed at 10 s and BWr eq was set at 16 Kbps by assigning each connection one out of 16 available TDMA time slots. Table 3. A comparison of the average residual energy of cluster heads, average end-to-end delay and packet delivery ratio PDR for different loads are obtained. Figure 3. In QBCDCP during the communication phase if the primary cluster head is depleted of energy, the entire cluster does not function and causes the WSN to become unstable and inconsistent.
This problem can be overcome by the dual cluster head model. In FTQAC, the cluster continues to work reliably since the secondary cluster head takes the role of the primary cluster head when the threshold E t energy is reached. In Fig. This model of dual cluster head has the feature of fault-tolerance and improves the robustness of the WSN.
From Fig. In this evaluation, we change the packet arrival rate at the source node and measure the endto-end delay. As expected, the increase in network load produces a higher queuing delay at each cluster head along a path, which gives a larger end-to-end delay. The base station sets up paths based on the energy of the cluster heads. If a cluster head with low residual energy is selected for the QoS path, this results in drop of the link during the communication phase and affects the desired QoS.
In FTQAC, the dual cluster head model ensures the necessary energy level and the bandwidth required for maintaining the link from base station to requesting cluster head node. The packet delivery ratio is defined as the number of packets generated by the source to the number of packets received by the destination node.
In FTQAC, the role transfer from primary cluster head to secondary cluster head ensures that the scheduled connection is not dropped, thereby maintaining the packet delivery ratio. The protocol achieves QoS routing in Wireless Sensor Networks by using delay and transmission energy as the routing metrics.
The protocol achieves fault tolerance through a dual cluster head mechanism and guarantees the desired QoS. Evaluated results show an increase in lifetime of the WSN. Abraham, Fapojuwo and Alejandra Cano-Tinoco: energy consumption and message delay analysis of QoS enhanced base station controlled dynamic clustering protocol for wireless sensor networks. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy efficient communication protocol for wireless microsensor networks, in Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, vol.
Balakrishnan, An application-specific protocol architecture for wireless microsensor networks. Manjeshwar, D. Agrawal, A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks, in Proceedings of the 2nd International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing Ft.
Lauderdale, Fl, , pp. Lindsey, C. Smaragdakis, I. Matta, A. Bestavros, SEP: a stable election protocol for clustered heterogeneous wireless sensor networks, in Proceedings of the 2nd International Workshop on Sensor and Actor Network Protocols and Applications , pp. Loscri, S. Marano, G. Chen, W. Li, H. Shou, B. Muruganathan, D. Ma, R. Bhasin, A. Fapojuwo, A centralized energy-efficient routing protocol for wireless sensor networks. Haiping, W. Ruchuan, Clustered-control algorithm for wireless multimedia sensor network communications, in Proceedings International Conference on Communications and Mobile Computing , pp.
Ji, C. Wu, Y. Zhang, F. Feng, X. Yu, Z. Liu, C. EkbataniFard, R. Monsefi, M. Akbarzadeh-T, M. Ben-othman, L. Mokdad, B. Aslam, W.
Phillips, G. Melodia, I. Noori, M. Ardakani, Lifetime analysis of random Event-Driven clustered wireless sensor networks. Yao, C. Lin, Y. Tian, L. Chen, A. Speer, M. Eltoweissy, Adaptive fault-tolerant QoS control algorithms for maximizing system lifetime of query-based wireless sensor networks. Dependable Secur. Prakash, K. Failures range from simple crash faults where a node becomes temporarily inactive to battery exhaustion resulting in node failures.
The nature of real-time applications creates significant challenges for sensor networks to maintain a high Quality of Service. Therefore, efficient fault detection and detachment have become essential for WSNs and we address these challenges in this chapter. Duche et al. The QoS of the network is affected by the failure of sensor nodes. Probability of sensor node failure increases with an increasing number of sensors.
In order to maintain QoS paths under failure conditions, identifying and detaching such faults becomes essential. The traffic is redirected to the working sensors and the QoS level is maintained throughout the duration of the connection as shown in Fig. Path redundancy technique to detect faulty sensor node is suggested in [3, 4].
Redundancy multiplies the energy consumption and lowers the number of right responses in sensor network lifetime. Many redundant paths in the sensor networks affect the rate of fault detection. In [5], link failure detection based on monitoring cycles MCs and monitoring paths MPs is presented. The constraints of this method are monitoring locations and separate wavelength for each monitoring cycle. Lee et al. Faulty sensor nodes are found based on the correlation between neighboring nodes and publishing of the result contrived at each node.
Time redundancy is employed to endure short term faults in sensing and communication process. To speed up the process, a sliding window is selected with storage for historical values. Cluster head failure recovery algorithm used in [7] to identify the faulty node has data loss issues, which develop on transfer of cluster head.
Lau et al. In this method, computation is not executed in individual sensor node and has no added power load to the sensor node. The algorithm examines the genuine sensing measurement loss and adopts Markov processes for padding in lost data. The knowledge of evidence fusion rules established on information entropy theory and degree of disagreement function aids to raise the efficiency of fault detection. Mitra et al. Fault detection algorithm, improved network lifetime, and self fault checking in Wireless Sensor Networks are proposed.
Mahapatro et al. A fuzzy logic based approach is employed to select the leading compromised result on the Pareto front. In [12], they present an online fault diagnosis algorithm for Wireless Sensor Networks, that considers the probability of faults in different parts of the sensor networks. ARF identifies faults by estimating the packet failure from parent nodes, and the algorithm reduces the routing gap to alert the neighbor nodes of the fault.
The protocol achieves fault tolerance and energy efficiency through a dual cluster head mechanism and guarantees the desired QoS by including delay and bandwidth parameters in the route selection process. Shin et al. The algorithm aids in substituting lesser sensor nodes and utilize the routing paths, improving the WSN lifetime, and decreasing the substitution cost.
Chatzigiannakis et al. Stress is given on data correctness induced by malicious nodes. The proposed approach employs Principal Component Analysis together on various metrics collected from different sensors.
This method combines related sensor data in a shared manner to detect faults among many sensors and assimilate the outcome from multiple groups of nodes.
The goal of using ETX is to find the route with the highest probability of packet delivery, instead of the shortest path. It is one of the favored routing metrics because it has good accuracy in determining link quality. The forward delivery ratio, d f is the measured probability that a data packet successfully arrives at the recipient; the reverse delivery ratio, dr is the probability that the ACK packet is successfully received.
The delivery ratios d f and dr are estimated using dedicated link probe packets. Each node broadcasts link probes of a fixed size, at an average period.
Calculation of a links ETX requires both d f and dr. Each probe sent by a node i contains the number of probe packets received by i from each of its neighbors during 4. This allows each neighbor to calculate the d f to i whenever it receives a probe from i.
To calculate RTT, a node sends a probe packet carrying a time stamp to each of its neighbors. Each neighbor immediately responds to the probe with a probe acknowledgment, echoing the time stamp.
This enables the sending node to measure round trip time to each of its neighbors. The fault detection analysis time increases immensely with larger numbers of sensor nodes. They are selected by ignoring the two consecutive paths, after each selected linear path.
You can install SQL express on how many cores you want it will utilize as per its limitation, so not an issue. This goes for all express edition as per my knowledge. Please mark this reply as the answer or vote as helpful, as appropriate, to make it useful for other readers. SQL Express as the below limitation and its free. Refer the below link. The content you requested has been removed.
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