Sign In. Access provided by: anon Sign Out. It assesses the suitability of the standard for the development and deployment of wireless sensor networks as well as providing guidance and insight into the relative advantages and disadvantages of various performance solutions.
Investigates multi-cluster networks and compares how they can be implemented. Analyzes the performance of a single cluster under different traffic and power management regimes including uplink vs. Discusses security issues in WPANs such as different security threats, their impact on performance, standard security mechanisms, and security policies. Threshold k is helpful to increase throughputs.
Wireless Personal Area Networks: Performance, Interconnection, and Security with IEEE Book Abstract: Wireless Personal Area Networks provides an . Wireless Personal Area Networks provides an in-depth analysis of the recent IEEE Networks: Performance, Interconnection and Security with IEEE
The solution performance evaluation is carried out under the NS2 simulator [ 22 ]. In all simulation, we have considered the followings.
The physical layer consists of IEEE The application layer includes three CBR traffic sources with different data rate and one sink in this article. Burst traffic is generated from the camera that capturing event photo and sending it to sink immediately.
We dynamically adapt BO, SO value to investigate the performance. Average end-to-end delay is one of the most important metrics to emergent events. In WSNs, the end-to-end delay is the total time delay to deliver a packet from source to sink node. It is the sum of delays at all links within the end-to-end path.
The delay at an intermediate node usually includes the following components: processing delay, queuing delay, transmission delay, propagation delay, and retransmission delay. We mainly consider the average end-to-end delay for all source traffic along a multihop path to sink node. By decreasing the packet retransmission, we can decrease the average end-to-end delay.
We use packet delivery ratio PDR to denote the performance. The coordinator consumes the energy when it transmits beacon and ACK packet, receives data packet, and listens the channel. Where rxPower is power consumption in receiving a packet, txPower is power consumption in transmitting a packet, sleepPower is power consumption in sleep state, and idlePower is power consumption in idle state. To measure the energy consumption in our scenarios, we use the energy model in NS For accelerating the power consumption in our simulation, we modify the default values of the NS2 default value [ 17 ].
There are three scenarios in this study, namely, chain topology, tree topology, and star topology. The simulation parameters are summarized in Table 4.
Simulation duration is set to 60 s of which the first 5 s allow the nodes to associate with the PAN coordinator, and the remaining time is used for sending application traffic. Each sensor node is set m away from the other nodes. Each node's queue size is set to 50 packets. For each configuration, we vary the inter-arrival time of the flows in source node to have different offered loads, assuming a constant packet size. With high offered loads, it causes higher packet loss because of the queue full.
With low offered loads, it causes the increase in the average end-to-end delay. Table 5 shows the PDR and average end-to-end delay Avg.
https://staskonisra.tk Source node cannot transmit data successfully due to long BI and inactive period. When BO value is small, the probability of collision is increased.
The bottom of Table 6 shows when BO is fixed, higher SO values have lower average end-to-end delay because of inactive period decreased. Sensor nodes do not change into sleep mode when BO equals to SO. We set BO, SO value to 15 to evaluate the performance in the non-beacon-enabled mode. Simulation time is 60 s and queue size is 50 packets.
Table 7 shows that all metrics packet delivery ratio, end-to-end delay, and energy consumption have not any differences between DropTail and BOB-RED algorithm.
Node which acquires the channel can transmit data immediately, so the queue length always is empty. The PDR value is only In the second set of experiments, we study the effect of dynamic adaptation scheme. Figure 14 illustrates the network topology with four nodes defined using NS-2 n0, n1, n2, n3. Each sensor node is set 20 m away from the other nodes.
We assume node 1, node 2 does not affect each other. Simulation parameters are all the same as shown in Table 4. DropTail is the queue management scheme. Dynamic adaptation scheme changes vary BO, SO values when burst traffic load comes. Table 8 shows the comparison of default BO, SO value with dynamic adaptation scheme in the average end-to-end delay, PDR, and energy consumption metrics. The average end-to-end delay is 0. When the simulation ends, coordinator node 0 still remains 3.
Conversely, residual energy of coordinator node 0 runs out at For investigating the effect of our scheme in multihop environment, the modified star topology is used. We design three traffic flows along coordinator to sink node not only one hop to coordinator like standard. The topology of this scenario is shown in Figure Node 1 sends CBR traffic from 10 to 55 s.
Node 2 sends CBR traffic from 25 to 40 s. Node 3 sends CBR traffic from 40 to 55 s. The focus of the experiment is the head of the bottleneck link between n0, n4 and its buffer in particular. Node 0 is the intermediate gateway node with DropTail or BOB-RED queue implemented, with a buffer capacity of 50 packets and a queue fully monitored during the simulation. BOB-RED operation is based on the principle that as the probability of a packet being dropped increases, the possibility of this packet being enqueued decreases.
We observe queue length at the gateway node 0 which is closest to the sink node and measuring the end-to-end delay from source node to sink node. By measuring the queue length of coordinator node n0 , we can observe that queue occupy is not heavy. If the traffic load is getting heavier, the amount of packets stay in the queue will increase. BOB-RED can lessen the congestion by early detecting the queue status to drop packet to decrease the retransmission of arriving packets. Table 9 shows the results. Figure 17 shows the energy consumption with different queue management scheme.
Conversely, both two mechanisms consume energy rapidly when traffic load is getting heavier from 25 to 40 s. In this article, we evaluate several queue management algorithms with respect to their abilities of maintaining high resource utilization and low energy consumption in IEEE The characteristics of different algorithms are also discussed and compared.
Simulation results show that RED queue management scheme accompanied with an appropriate setting of BO, SO can effectively achieve a better performance. Despite the vary kinds of experiments have done in this article, how to correctly decide the BOB-RED parameters and BO, SO values according different kind of traffic loads is still an issue. In our future study, we will observe mobile sink and sensor nodes as possible.
How the nodes mobility model and velocity to affect the end-to-end delay, packet delivery ratio, and better energy consumption in multimedia services over IEEE PART IEEE Std Test and Application. Wireless Communications and Mobile Computing Conference, IWCMC ' International , Floyd S, Jacobson V: Random early detection gateways for congestion avoidance.
Lin D, Morris R: Dynamics of random early detection. Cannes, France; Shreedhar M, Varghese G: Efficient fair queuing using deficit round-robin. Int J Comput Netw Commun , 2 2 Comput Commun J , 28 10 Communication Systems, ICCS IEEE 65 th. A discussion on a potential low power, low bit rate standard. Kleinrock L: Queueing Systems.